Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "160"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 160 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 33 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 31 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 160, Node N13:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459848 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 14.544176 16.348441 29.716320 32.001242 14.318278 21.786876 3.154317 5.213471 0.0396 0.0412 0.0029 1.186082 1.185780
2459847 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 16.686356 18.889426 27.698818 30.163325 21.930425 28.381648 1.530605 2.251718 0.0372 0.0381 0.0026 1.223717 1.220928
2459845 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 18.404256 20.987188 39.394164 41.369757 10.126320 16.539877 2.293192 2.825822 0.0446 0.0485 0.0053 1.362318 1.366602
2459844 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.091185 13.915568 5.853268 7.409404 4.277949 6.007770 10.114761 15.335812 0.0328 0.0305 0.0029 nan nan
2459843 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 19.182981 21.211696 19.140796 20.442377 64.312300 70.847405 2.142056 3.582372 0.0414 0.0438 0.0042 1.275682 1.273061
2459842 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 11.303707 14.483696 8.859457 9.973024 -0.042775 -0.448560 1.063479 1.960693 0.0389 0.0397 0.0043 1.317742 1.302071
2459841 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.104321 14.167143 4.107819 5.064672 5.297341 7.784313 8.587265 11.890570 0.0325 0.0300 0.0033 nan nan
2459839 digital_ok 100.00% - - - - - 0.199533 1.976470 -0.761646 -0.515474 1.390033 3.301249 8.463737 11.924618 nan nan nan nan nan
2459838 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 15.941095 17.777246 22.361649 23.752038 19.770641 29.646752 1.807418 2.772428 0.0439 0.0479 0.0036 1.232404 1.224637
2459836 digital_ok - 100.00% 100.00% 0.00% - - nan nan nan nan nan nan nan nan 0.0632 0.0660 0.0173 nan nan
2459835 digital_ok 0.00% 100.00% 100.00% 0.00% - - 2.416998 2.902812 2.268922 2.665449 -0.136663 0.887985 0.599845 1.081130 0.0600 0.0674 0.0146 nan nan
2459833 digital_ok 100.00% 100.00% 100.00% 0.00% - - 5.959607 6.565891 3.125381 3.324654 3.567818 6.173137 9.109719 12.779395 0.0449 0.0510 0.0107 nan nan
2459832 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% nan nan inf inf nan nan nan nan nan nan nan 0.000000 0.000000
2459831 digital_ok 100.00% 100.00% 100.00% 0.00% - - -0.113219 1.872950 -0.901630 -0.369513 1.424017 2.928742 5.780967 8.066157 0.0563 0.0925 0.0203 nan nan
2459830 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 31.253183 32.076582 35.704346 37.102279 39.994168 36.262249 6.105912 7.506237 0.0412 0.0419 0.0029 1.264942 1.259351
2459829 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 29.141939 32.805982 29.091690 30.341797 28.917374 32.019344 8.573902 10.410573 0.0422 0.0445 0.0015 inf inf
2459828 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 25.449237 26.661153 31.370412 32.438663 36.774058 33.282024 13.586753 14.796774 0.0417 0.0447 0.0040 0.000000 0.000000
2459827 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 22.502059 24.394398 35.323948 36.944466 24.703474 26.175823 1.247677 1.664350 0.0393 0.0432 0.0033 0.000000 0.000000
2459826 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 23.823709 24.275278 39.617422 40.775900 49.107870 45.173105 8.136608 9.395173 0.0412 0.0441 0.0037 0.000000 0.000000
2459825 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 26.347503 26.599889 31.553474 32.572746 27.721921 25.689793 0.434135 0.611726 0.0401 0.0412 0.0021 1.287974 1.247732
2459824 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 14.845589 18.038665 25.193809 26.447634 10.771754 19.253869 2.765169 3.757095 0.0387 0.0405 0.0029 1.373032 1.364727
2459823 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 23.435476 22.959861 47.074828 48.240564 34.474761 36.057551 29.155232 34.085500 0.0388 0.0419 0.0043 1.182568 1.197078
2459822 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 24.533778 24.735888 43.108786 44.392296 30.923641 29.592753 1.261705 1.303411 0.0403 0.0427 0.0038 1.187225 1.194239
2459821 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 27.204993 27.897664 44.120560 45.072411 26.487706 26.131344 -0.595958 -0.075803 0.0405 0.0421 0.0028 1.235754 1.232023
2459820 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% nan nan inf inf nan nan nan nan nan nan nan 0.000000 0.000000
2459817 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 22.181960 22.621504 42.874902 43.854061 36.398132 36.567543 1.445781 1.751300 0.0441 0.0450 0.0019 1.205479 1.203246
2459816 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 17.435279 18.502754 43.013929 44.613249 46.813250 45.374868 8.051447 10.609346 0.0434 0.0445 0.0027 1.207263 1.204377
2459815 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 20.456407 20.274497 46.893999 48.133877 47.672105 48.031407 11.402206 14.064979 0.0420 0.0430 0.0009 1.206402 1.206572
2459814 digital_ok 0.00% - - - - - nan nan nan nan nan nan nan nan nan nan nan nan nan
2459813 digital_ok 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% nan nan inf inf nan nan nan nan nan nan nan 0.000000 0.000000

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 160: 2459848

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 32.001242 16.348441 14.544176 32.001242 29.716320 21.786876 14.318278 5.213471 3.154317

Antenna 160: 2459847

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 30.163325 18.889426 16.686356 30.163325 27.698818 28.381648 21.930425 2.251718 1.530605

Antenna 160: 2459845

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 41.369757 20.987188 18.404256 41.369757 39.394164 16.539877 10.126320 2.825822 2.293192

Antenna 160: 2459844

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Temporal Discontinuties 15.335812 12.091185 13.915568 5.853268 7.409404 4.277949 6.007770 10.114761 15.335812

Antenna 160: 2459843

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Temporal Variability 70.847405 21.211696 19.182981 20.442377 19.140796 70.847405 64.312300 3.582372 2.142056

Antenna 160: 2459842

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Shape 14.483696 11.303707 14.483696 8.859457 9.973024 -0.042775 -0.448560 1.063479 1.960693

Antenna 160: 2459841

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Shape 14.167143 12.104321 14.167143 4.107819 5.064672 5.297341 7.784313 8.587265 11.890570

Antenna 160: 2459839

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Temporal Discontinuties 11.924618 1.976470 0.199533 -0.515474 -0.761646 3.301249 1.390033 11.924618 8.463737

Antenna 160: 2459838

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Temporal Variability 29.646752 17.777246 15.941095 23.752038 22.361649 29.646752 19.770641 2.772428 1.807418

Antenna 160: 2459835

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Shape 2.902812 2.902812 2.416998 2.665449 2.268922 0.887985 -0.136663 1.081130 0.599845

Antenna 160: 2459833

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Temporal Discontinuties 12.779395 6.565891 5.959607 3.324654 3.125381 6.173137 3.567818 12.779395 9.109719

Antenna 160: 2459832

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok ee Shape nan nan nan inf inf nan nan nan nan

Antenna 160: 2459831

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Temporal Discontinuties 8.066157 -0.113219 1.872950 -0.901630 -0.369513 1.424017 2.928742 5.780967 8.066157

Antenna 160: 2459830

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok ee Temporal Variability 39.994168 31.253183 32.076582 35.704346 37.102279 39.994168 36.262249 6.105912 7.506237

Antenna 160: 2459829

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Shape 32.805982 32.805982 29.141939 30.341797 29.091690 32.019344 28.917374 10.410573 8.573902

Antenna 160: 2459828

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok ee Temporal Variability 36.774058 26.661153 25.449237 32.438663 31.370412 33.282024 36.774058 14.796774 13.586753

Antenna 160: 2459827

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 36.944466 22.502059 24.394398 35.323948 36.944466 24.703474 26.175823 1.247677 1.664350

Antenna 160: 2459826

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok ee Temporal Variability 49.107870 24.275278 23.823709 40.775900 39.617422 45.173105 49.107870 9.395173 8.136608

Antenna 160: 2459825

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 32.572746 26.599889 26.347503 32.572746 31.553474 25.689793 27.721921 0.611726 0.434135

Antenna 160: 2459824

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 26.447634 14.845589 18.038665 25.193809 26.447634 10.771754 19.253869 2.765169 3.757095

Antenna 160: 2459823

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 48.240564 22.959861 23.435476 48.240564 47.074828 36.057551 34.474761 34.085500 29.155232

Antenna 160: 2459822

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 44.392296 24.533778 24.735888 43.108786 44.392296 30.923641 29.592753 1.261705 1.303411

Antenna 160: 2459821

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 45.072411 27.897664 27.204993 45.072411 44.120560 26.131344 26.487706 -0.075803 -0.595958

Antenna 160: 2459820

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok ee Shape nan nan nan inf inf nan nan nan nan

Antenna 160: 2459817

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 43.854061 22.181960 22.621504 42.874902 43.854061 36.398132 36.567543 1.445781 1.751300

Antenna 160: 2459816

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok ee Temporal Variability 46.813250 18.502754 17.435279 44.613249 43.013929 45.374868 46.813250 10.609346 8.051447

Antenna 160: 2459815

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Power 48.133877 20.274497 20.456407 48.133877 46.893999 48.031407 47.672105 14.064979 11.402206

Antenna 160: 2459814

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 160: 2459813

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
160 N13 digital_ok nn Shape nan nan nan inf inf nan nan nan nan
In [ ]: